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Family-forest Owners’ Willingness
to Harvest Sawlogs and Woody
Biomass: The Effect of Price on
Social Availability
Francisco X. Aguilar, Marissa “Jo” Daniel, and Zhen Cai
Understanding willingness to harvest (WTH) is essential to assessing the social
availability of woody biomass from private land. Currently, the only economically
feasible way to harvest woody biomass is in conjunction with sawlogs. We examined
WTH sawlogs and woody biomass from owners of family forests using data from
a survey of Missouri forest owners. While their WTH increased with revenue
expected from woody biomass, revenue expected from sawlogs was a stronger
in(cid:976)luence. Incentive payments for woody biomass thus are unlikely to increase
its supply, and the social availability of woody biomass will remain limited unless
sawlog prices rise signi(cid:976)icantly.
Key Words: integrated harvest, ordered choice, public incentive, sawlog, woody
biomass
Use of woody biomass to generate bioenergy has been the subject of much
discussion in the scienti(cid:976)ic literature in recent years. Woody biomass generally
is de(cid:976)ined as “trees and woody plants, including limbs, tops, needles, leaves,
and other woody parts, grown in a forest, woodland, or rangeland environment
that are the by-products of forest management” (U.S. Department of Agriculture
(USDA) Forest Service 2008, p. 16). A combination of the historically high price
for fossil fuel and public policies aimed at addressing the United States’ energy
independence and climate change has been a major driver of this research
thrust (Aguilar and Garrett 2009). Research in the forestry sector has primarily
focused on biophysical assessments, analyses of the economic feasibility of
providing biomass for fuel, and the social availability of such biomass.
Investigative efforts such as the Billion-ton Biomass Report by the U.S.
Departments of Agriculture and Energy in 2005 (Perlack et al. 2005) and
its update in 2011 (U.S. Department of Energy 2011) have aimed to provide
an overview of the potential biophysical availability of woody biomass for
renewable power and biofuel. State-level studies of the potential supply of such
biomass have also been conducted (Becker et al. 2010). Regional assessments,
such as Goerndt et al. (2012), have explored potential availability in the vicinity
of power plants to calculate the maximum sustainable capacity to generate
Francisco Aguilar is associate professor and Zhen Cai is a postdoctoral fellow in the Department
of Forestry in the School of Natural Resources at University of Missouri. Marissa “Jo” Daniel is
a forestry technician with the U.S. Department of Agriculture Forest Service. Correspondence:
Francisco Aguilar  Department of Forestry, School of Natural Resources  University of Missouri 
Columbia, MO 65211  Phone [phone]  Email [email protected].
This study was partially funded by the U.S. Department of Agriculture Natural Resource
Conservation Service under agreement 69-6424-9-214 and by the USDA Forest Service Wood
Education and Resource Center under agreement 09-DG-11420004-293. The views expressed are
the authors’ and do not necessarily represent the policies or views of the sponsoring agencies.
Agricultural and Resource Economics Review 43/2 (August 2014)
Copyright 2014 Northeastern Agricultural and Resource Economics Association
2 August 2014 Agricultural and Resource Economics Review
renewable power from forests. Galik, Abt, and Wu (2009), for example,
identi(cid:976)ied large quantities of forest residue in the southeastern United States
but warned of the long-term feedstock supply that would be needed to meet
continuous energy demands.
A number of economic analyses have evaluated the feasibility of using woody
biomass to produce various forms of energy. Those studies have found that
such production is economically feasible only when the biomass is harvested
in conjunction with sawlogs (commonly referred to as an integrated harvest)
because of the high cost associated with collecting the low-density biomass
material (Hall 1997, Hubbard et al. 2007, Saunders et al. 2012). Saunders et al.
(2012) determined break-even points for integrated harvests and estimated a
maximum procurement distance of about 140 kilometers from a power plant.
White, Alig, and Stein (2010) identi(cid:976)ied wood from municipal solid waste
facilities, milling residue, and some timber harvest residue as the woody
biomass feedstocks most likely to be used for energy since they are relatively
inexpensive to procure. Harvesting of logging residue can generate new jobs and
stimulate rural economies by creating demand for traditionally unmarketable
materials. This, in turn, has important implications for forest management;
the biomass previously left on the ground could be removed, which would
reduce fuel loads and enhance wildlife habitat for some species (Aguilar and
Garrett 2009). Moreover, the resulting biofuel could simultaneously promote
greater energy independence and reduce concerns regarding use of food crops
as feedstocks (e.g., corn) (Skipper et al. 2009, Bartuska 2010). Consequently,
the overall economic impact of harvesting and converting woody biomass to
energy could be substantial (Perez-Verdin et al. 2008).
The social availability of woody biomass relates to factors that in(cid:976)luence
landowners’ willingness to harvest (WTH) materials for bioenergy. Social
availability was de(cid:976)ined by Butler et al. (2010, p. 151) as “the social factors
that determine the desirability of the potential goods and services and the
propensity for those who control a resource, such as wood, to use it themselves,
allow others to do so, or do nothing with it.” However, the literature so far has
not thoroughly evaluated the social availability of woody biomass because
the concurrent price of sawlogs has not been incorporated into estimates of
landowners’ WTH. For example, Markowski-Lindsay et al. (2012) elicited
preferences of owners of family forests in Massachusetts for woody biomass
harvesting by asking about their willingness to accept an offer to harvest timber
and/or woody biomass but included only the price for woody biomass in the
explanatory variables, leaving out timber prices. Likewise, Joshi and Mehmood
(2011) elicited WTH woody biomass among nonindustrial private forest owners
in Arkansas, Florida, and Virginia but did not include revenue for sawlogs as an
explanatory variable (they included an ordinal explanatory variable capturing
the importance of timber production objectives to land ownership). Moreover,
the effect of public policies that provide incentive payments for biomass
has not been evaluated. The existing literature has demonstrated that forest
owners’ decisions regarding harvesting are in(cid:976)luenced by (cid:976)inancial incentives
(e.g., Kurtz and Lewis 1981). One program designed to increase the social
availability of biomass is the USDA Farm Service Agency’s (2012) Biomass Crop
Assistance Program (BCAP), which was originally introduced in the 2002 Farm
Bill (Public Law 107-171) and later amended under the 2008 Farm Bill (Public
Law 110-234). BCAP allows landowners to receive matching payments for
quali(cid:976)ied biomass crops.
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Aguilar, Daniel, and Cai Owners’ Willingness to Harvest Sawlogs and Woody Biomass 3
This research explores family-forest owners’ WTH sawlogs and woody
biomass as a function of select explanatory factors. Speci(cid:976)ically, we evaluate the
effect of (i) sawlog and woody biomass revenues from an integrated harvest,
(ii) public support payments, and (iii) land resource and socioeconomic
characteristics on family-forest owners’ WTH woody biomass. We concentrate
on the marginal effects of sawlog and woody biomass revenue to determine
the likelihood that a family-owned forest will be harvested for both products
and are particularly interested in the social availability of woody biomass.
Empirically, we address these questions by analyzing data collected from
owners of family-forest properties in Missouri, where 83 percent of the
forests are privately owned and there is a considerable swath of forest that is
overstocked and could bene(cid:976)it from integrated harvesting. In addition, there is
no market for woody biomass or comparable materials in Missouri (because
there is no pulp industry there), and revenue from sales of woody biomass in
a bioenergy market could offset some of the cost associated with reducing the
basal area in overstocked stands and enhancing wildlife habitat (Moser, Piva,
and Treiman 2012, Shi(cid:976)ley et al. 2012, Aguilar, Daniel, and Narine 2013).
Theoretical Framework
Our study of family-forest owners’ WTH sawlogs for timber and woody
biomass for energy is based on a review of the literature on landowner harvest
decisions (e.g., Kurtz and Lewis 1981, Becker et al. 2010, Joshi and Mehmood
2011, Amacher, Conway, and Sullivan 2003) and rooted in random utility
theory. As a decision-maker, a family-forest owner aims to maximize utility (U)
from a decision about whether to conduct an integrated harvest. However, the
ith owner’s utility may not derive simply from the potential revenue generated
from selling sawlogs and biomass. Kurtz and Lewis (1981), for example,
suggested that decisions by owners in the Missouri Ozarks to engage in forest
management were also correlated with their ownership motivations and
objectives and by constraints on the parcel of woodland. Hence, we assume
that maximization of utility (U) is not solely a function of revenue and other
monetary incentives (P). A landowner instead will maximize welfare, which is
a function of monetary and nonmonetary factors that include the landowners’
demographic characteristics (D) (Amacher, Conway, and Sullivan 2003, Butler
et al. 2007, Joshi and Arano 2009), reasons for owning forest land (O) (Binkley
1981, Kurtz and Lewis 1981, Kline, Alig, and Johnson 2000, Gruchy et al. 2012),
perceptions about harvesting and bioenergy (B) (Becker et al. 2010, Joshi and
Mehmood 2011), and land management practices (M) (Joshi and Arano 2009)
plus the biophysical characteristics of the land (L) (Joshi and Mehmood 2011,
Markowski-Lindsay et al. 2012). Thus, the utility derived by the ith family-forest
owner from harvesting can be expressed as a function of various explanatory
factors:
(1) U = f {P, D, O, B, L, M}.
i i i i i i
The actual utility derived from harvesting of forests, U*, is not observable, but
i
the explanatory factors’ coef(cid:976)icients can be estimated in a latent variable model:
(2) U* = X β +  X  Normal (0,1).
i i,k+1 i
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In the model, U* is a latent variable representing the ith individual’s utility
i
derived from an integrated harvest, X is an information matrix with k
i,k+1
variables, the intercept β is a vector of coef(cid:976)icients capturing the effects of
explanatory factors, and  is the random error term. Since we could not observe
U* directly, we collected forest owners’ WTH by way of their responses to a
i
survey regarding an offer to harvest their forests’ sawlogs and woody biomass.
The survey used a (cid:976)ive-point ordinal rating scale (1 = I would de(cid:976)initely
not accept this offer, 2 = I would probably not accept this offer, 3 = I would
probably accept this offer, 4 = I would very likely accept this offer, 5 = I would
de(cid:976)initely accept this offer) and a monetary public incentive (described later
under methods). The ordered response for this latent variable model assumed
the following relationship:
1 if U*  μ
i 1
2 if μ < U*  μ
1 i 2
(3) WTH = 3 if μ < U*  μ
2 i 3
4 if μ < U*  μ
3 i 4
5 if μ  U*
4 i
in which WTH is the ith respondent’s rating for a particular harvest offer and
μ represents unknown threshold parameters of cut points between preference
levels. Under an assumption of a normally distributed random error,
(4) Prob(WTH = 1) = Φ(–x ʹβ)
ik
Prob(WTH = 2) = Φ(μ – x ʹβ) – Φ(–x ʹβ)
1 ik ik
Prob(WTH = 3) = Φ(μ – x ʹβ) – Φ(–x ʹβ)
2 ik ik
Prob(WTH = 4) = Φ(μ – x ʹβ) – Φ(–x ʹβ)
3 ik ik
Prob(WTH = 5) = 1 – Φ(μ – x ʹβ)
4 ik
where Φ denotes the normal cumulative distribution and β corresponds
to regression coef(cid:976)icients of the k independent variables in the model. The
coef(cid:976)icients in the model are estimated using maximum likelihood (McKelvey
and Zavoina 1975, Hausman and Ruud 1987, Wooldridge 2002).
Ordinal scales are commonly used to capture stated preferences for a speci(cid:976)ic
issue (Beggs, Cardell, and Hausman 1981, Getzner and Grabner-Krauter 2004),
which in this case is family-forest owners’ willingness to conduct an integrated
harvest given a particular offer. Ordinal scales gather more information from
a single observation than choice-based models, which only identify the most
preferred alternative (Hausman and Ruud 1987). A motivation for using an
ordinal scale rather than a binary choice in the survey was to reduce both
uncertainty effects in the estimation and respondent fatigue. Several studies
have discussed stated-preference uncertainty in designed scenarios (e.g.,
contingent valuation), including Shaikh, Sun, and van Kooten (2007), Akter,
Bennett, and Akhter (2008), and Markowski-Lindsay et al. (2012), who
accounted for this issue when estimating the WTH of family-forest landowners.
Use of an ordinal scale better captures stated-preference uncertainty when
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Aguilar, Daniel, and Cai Owners’ Willingness to Harvest Sawlogs and Woody Biomass 5
Representa(cid:415) ve forest stand in the study area
A(cid:332) er tradi(cid:415) onal commercial (cid:415) mber A(cid:332) er integrated commercial (cid:415) mber and
harves(cid:415) ng woody biomass harves(cid:415) ng
Figure 1. Photographs of Forest Stands Included in the Survey
using a single construct as opposed to a two-step approach as suggested by
Champ et al. (1997) in which respondents’ (un)certainty is recorded following
their responses to a scenario.1 Ordered models have also been applied in
studies eliciting WTH preferences of nonindustrial private landowners (e.g.,
Gruchy et al. 2012).
Methods
Questionnaire and Model Variables
The survey instrument eliciting Missouri family-forest owners’ WTH sawlogs
and woody biomass in an integrated harvest was based on prior studies and
our theoretical framework. We included a de(cid:976)inition of woody biomass and
its applications in bioenergy generation in the survey to avoid potential bias
caused by variations in respondents’ knowledge of woody biomass (Joshi et al.
2013). We also included a description of an integrated harvest accompanied
by three photographs that depicted a forested stand that was representative of
the study region: (i) preharvest, (ii) after a traditional commercial harvest of
sawlogs only, and (iii) after an integrated harvest (see Figure 1).
1 The two-step approach and an ordinal response were tested in focus groups. We chose to
use the latter because of its simplicity and the ability of respondents to report uncertainty in a
single answer. Past studies that used both approaches (e.g., Gruchy et al. 2012) determined that
the coef(cid:976)icients obtained from ordinal logit, binary logit, and Tobit regressions for forest owners’
WTH biomass were consistent.
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According to the USDA Forest Service’s Forest Inventory and Analysis (FIA)
database, a representative acre of forest is capable of producing an average
of about 4,000 board feet (bf) of saw timber and 30 short tons (1 short ton
equals 0.907 metric tons) of green woody biomass (Miles 2011). However,
clear-cutting (removing every standing tree) is not commonly used because of
the region’s hilly landscape and shallow soils (Beilmann and Brenner 1951).
A representative silvicultural aim of an integrated harvest is to remove 50
percent of the saw timber and woody biomass, which is thus equivalent to 2,000
bf of timber and 15 short tons of green woody biomass per acre. We selected
this level of removal for our study based on recent integrated harvests in the
region (Saunders et al. 2012), consultation with local state agencies (Daniel
2012), and standards included in Missouri’s best management practices for
such harvests (Missouri Department of Conservation 2008). Unlike other
regions of the country where tree plantations (even-age forests) are the norm,
Missouri timberlands are predominantly (95 percent by area) uneven-age
broadleaf forests that are allowed to naturally regenerate following a harvest
(Moser et al. 2006).
The survey introduced nine hypothetical scenarios in the form of harvest offers
that differed in prices paid for sawlogs, woody biomass, and biomass incentive
payments and asked respondents to rate their willingness to accept the offers
(Adamowicz et al. 1998). The sawlog prices presented were based on stumpage
prices reported in Missouri’s Timber Price Trends quarterly reports published by
the state Department of Conservation (2012). We used a three-year average price
for oak species, the dominant species group in the region, to set the mid-level
price per bf of $0.1. The three-year average was used because of depressed prices
observed in recent years (Woodall et al. 2012). Revenue from product sales was
calculated on a per-acre basis as in Markowski-Lindsay et al. (2012). Based on
harvesting of 2,000 bf per acre, we set the mid-level sawlog price at $200 per acre
($494 per hectare) and minimum and maximum levels of $100 per acre ($247
per hectare) and $300 per acre ($741 per hectare).
Corresponding prices for woody biomass were set to represent local energy
markets with the mid-level biomass price based on an energy equivalence to coal
(Saunders et al. 2012). Our mid-level revenue for 15 short tons of green woody
biomass was $50 per acre ($123 per hectare) and the minimum and maximum
revenues were $25 per acre ($62 per hectare) and $75 per acre ($185 per
hectare). Notice that woody biomass prices are treated as being independent of
sawlog prices. This is a fair assumption in the short term, although any signi(cid:976)icant
change in demand for woody biomass in the future, and thus in its price, could
create price pressure for timber (Ince and Nepal 2012).
The public incentive payments were established following guidelines from
USDA’s Biomass Crop Assistance Program (USDA Farm Service Agency 2012).
Since USDA would match eligible dry short tons of woody biomass dollar for
dollar up to $45 per ton, we estimated that the average incentive price would be
$25 per green short ton per acre ($62 per hectare) with a minimum of $0 and a
maximum of $50 ($123 per hectare). The conversion factor for dry (moisture-
free) to green short tons per acre was 50 percent.
Table 1 summarizes the offers presented in the survey. For each variable,
there was a minimum, medium, and maximum level, creating a balanced
research design that addressed potential problems related to correlation
of attributes with the model intercept and differences in statistical power of
individual attributes (Lusk and Norwood 2005). With our three-level design,
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Aguilar, Daniel, and Cai Owners’ Willingness to Harvest Sawlogs and Woody Biomass 7
Table 1. Prices Offered for Harvested Sawlogs, Harvested Woody Biomass,
and Public Incentive Payments
Sawlogs Woody Biomass Public Incentive
$100 per acre $25 per acre None
$247 per hectare $62 per hectare
$200 per acre $50 per acre $25 per acre
$494 per hectare $123 per hectare $62 per hectare
$300 per acre $75 per acre $50 per acre
$741 per hectare $185 per hectare $123 per hectare
no individual variable possessed more weight than the other two, minimizing
design bias (Elfenbein and Ambady 2002).
We created the offers using a fractional orthogonal design in Bretton-Clark’s
conjoint designer program (Bretton-Clark 1988). The analysis software
generates subsets of pro(cid:976)iles based on speci(cid:976)ied attribute levels and orthogonal
production combinations, and those subsets minimize confounding of attribute
main effects. Nine price and public incentive pro(cid:976)iles, each representing an offer
in the survey, were generated from the 27 possible combinations (three saw
log prices times three biomass prices times three incentive payments). This
fractional factorial design overcomes information overload for respondents,
a frequent problem in complete factorial experiments (Green and Srinivasan
1990, Louviere, Hensher, and Swait 2000). To further reduce the risk of
information overload, we randomly selected three scenarios to present to each
respondent. Thus, as in Aguilar (2009), we had three versions of the survey,
each containing a unique set of three scenarios.
The scenarios mimicked the process that commonly occurs in Missouri;
landowners typically are approached by a logger with a particular offer. The
offers in our survey were presented in the following form:
You are approached with an offer from a professional logger to harvest
your woodlands following best management practices. The offer is (a)
$_____ per acre to harvest timber (sawlogs), (b) an additional $_____ per
acre to also remove 15 short tons of woody biomass from your property,
and (c) an additional $_____ per acre for a public incentive payment that
requires you to have a professional forest management plan by the time
of harvest. Would you seriously consider this offer and harvest part or all
of your property?
Participants responded to each offer using the (cid:976)ive-point WTH scale (1 =
would de(cid:976)initely not accept, 2 = would probably not accept, 3 = would probably
accept, 4 = would very likely accept, 5 = would de(cid:976)initely accept).
Table 2 presents a summary of the explanatory variables in the model.
The demographic variables included in the questionnaire section were age,
education, income, gender, and presence of children under the age of 18 in the
household (Amacher, Conway, and Sullivan 2003, Butler and Leatherberry 2004,
Joshi and Arano 2009, Young and Reichenbach 1987). Constructs that captured
respondents’ reasons for owning forest land were based on information in the
USDA Forest Service National Woodland Owner Survey (NWOS) discussed by
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8 August 2014 Agricultural and Resource Economics Review
Table 2. Explanatory Variables Used to Model Private Forest Owners’
Willingness to Harvest
Sources Variables Descriptions
Prices and Public Incentive
Missouri Department Timber price Continuous variables
of Conservation (dollars per acre)
2012;
Saunders et al. 2012; Biomass price
USDA Forest Service (dollars per acre)
2012
Public incentive price
(dollars per acre)
Demographicsa
Amacher, Conway, Age Equals 1 if older than 55 years;
and Sullivan 2003; Equals 0 otherwise
Butler and
Leatherberry 2004; Education Equals 1 if at least 4-year college
Joshi and Arano degree
2009; Equals 0 otherwise
Young and
Reichenbach 1987 Income of $50,000 per Equals 1 if annual household
year or moreb income from all sources of at
least $50,000
Equals 0 otherwise
Gender Equals 1 if male
Equals 0 otherwise
Children under 18 years Equals 1 if children under 18 live
live in household in household
Equals 0 otherwise
Reasons for Owning Forest Land
Butler et al. 2007; As a part of the farm or Equals 1 if not important
Marty, Kurtz, and ranch Equals 2 if slightly important
Gramann 1988; Equals 3 if moderately important
Broderick, Hadden, To pass land on to Equals 4 if very important
and Heninger 1994; children or other heirs Equals 5 if extremely important
Finley and Kittredge
2006 For production of sawlogs,
pulpwood, or other timber
products
Bioenergy Views
Gruchy et al. 2012; Supports harvesting Equals 1 if agree or strongly agree
Galik, Abt, and Wu woody biomass for energy with statement
2009; Equals 0 otherwise
Joshi and Mehmood Harvesting woody biomass
2011; is not likely to result in soil
Markowski-Lindsay erosion
et al. 2012
Continued on following page
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Aguilar, Daniel, and Cai Owners’ Willingness to Harvest Sawlogs and Woody Biomass 9
Table 2. (continued)
Sources Variables Descriptions
Biophysical Characteristics of the Land
Bliss and Martin 1989; Wooded parcel size Equals 1 if woodland is 1,000
Joshi and Arano 2009; acres or more in size
Erickson, Ryan, and Equals 0 otherwise
De Young 2002
Saw timber volumec Continuous; estimates in board
feet were divided by 100,000 to
downscale (cid:976)igures
Biomass volumed Continuous; estimates in short
tons were divided by 1,000 to
downscale values
Management Characteristics
Joshi and Arano Primary residence Equals 1 if woodland (or part of
2009; it) was located on a parcel
D’Amato et al. 2010; adjoining the primary
Greene and Blatner residence
1986 Equals 0 otherwise
Had sold timber since Yes = 1
owned No = 0
Had no plan to harvest in Yes = 1
the future regardless of No = 0
price
Had a professionally Yes = 1
written forest No = 0
management plan
Had sold timber since Yes = 1
owning the land and No = 0
planned to sell timber
in the future
a Both dummy coding and effect coding can be applied to demographic variables. In this study, we
applied dummy coding because of the ease of effect interpretation in this context. The dummy-variable
trap was solved by dropping one category of the variable from the model.
b The actual median household annual income in Missouri was $46,262 (U.S. Census Bureau 2012) but
the information collected was in $10,000 intervals. Respondents who selected income of $50,000 or
more were thus classi(cid:976)ied as exceeding the median. Interaction variables between income and harvest
revenue were generated to detect sensitivity across income levels.
c Estimates of standing saw timber volume were derived from the USDA Forest Service’s FIA database.
d Biomass volume estimates were derived from the USDA Forest Service’s FIA database.
Butler et al. (2007). We included three variables in the model to capture the
importance of woodland ownership: (i) as part of a farm, (ii) production of
wood products, and (iii) for bequests.
In the survey, respondents were asked to self-report the importance of
two statements related to bioenergy as a way of gathering their perceptions
of the impacts of harvesting woody biomass. The (cid:976)irst statement asked the
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